Note: The job is a remote job and is open to candidates in USA. Order.co is a B2B procurement and finance platform that uses embedded AI agents and financial products to automate purchasing and payment workflows. The Staff Applied AI Scientist will own the end-to-end AI and machine learning architecture, build and deploy applied AI systems, and connect evaluation and production performance to measurable business outcomes. The role also provides technical direction, establishes responsible AI and model operations practices, and prioritizes AI opportunities.
Responsibilities
- The AI and machine learning architecture. You will design the machine learning and agentic architecture end to end, covering model hosting and serving, prompt and model versioning, retrieval and embeddings, agent tooling, guardrails, and the evaluation framework that tells you whether any of it is working. You are the person who makes these calls and defends them
- Applied modeling and evaluation. You will choose between deterministic and large language model or agent-based approaches on the merits, with explicit trade-offs across accuracy, latency, cost, and reliability. You will build evaluation that connects offline and online quality to business outcomes and risk controls, rather than treating model metrics as the goal
- Production delivery and model operations. You will own the full lifecycle, including experimentation, versioning, continuous integration and deployment for both models and prompts, monitoring, drift detection, rollback, and incident readiness. You will define the rollout strategy and the likely failure modes before launch rather than after it
- Responsible AI in practice. You will build safety guardrails, hallucination mitigation, bias testing, and appropriate handling of sensitive data into the design itself, so that our data privacy and prohibited use commitments are met by construction rather than reviewed at the end
- The data requirements your AI depends on. You will specify what "AI-ready" means for each initiative, including the training and retrieval data, labeling, feature availability, and vector or search infrastructure you need, and you will work with data engineering and platform to make it real so that models run on governed infrastructure instead of shadow deployments
- Business outcomes and prioritization. You will turn ambiguous goals into technical bets with clear hypotheses and success criteria, and you will own a portfolio of AI opportunities by identifying the highest-leverage ones, sequencing them, and building the execution path rather than waiting for assigned work
- Technical direction and influence. You will advise product and engineering leadership on what is feasible, what it costs, what it risks, and what it is likely to return. You will set architecture and delivery patterns that other people reuse, and you will mentor experienced individual contributors on applied AI execution and production quality
Skills
- At least 10 years in applied data science, machine learning, or applied AI, with repeated delivery of production systems that moved a business metric you can name
- Ownership of AI and machine learning system architecture rather than models alone, including serving, retrieval, evaluation, guardrails, and the operational loop around them
- Real depth in current large language model and agent technology, meaning you know where it works, how it fails, how you evaluate it, and when a simpler deterministic approach is the better answer
- Demonstrated practice in machine learning operations, including versioning, continuous integration and deployment for models and prompts, monitoring, drift detection, and rollback
- Portfolio-level ownership, where you have prioritized across competing AI opportunities and built the path forward rather than only executing a roadmap that someone else set
- Heavy daily use of AI-native engineering workflows across design, coding, debugging, and review for at least the past 18 months, along with the judgment to know where to verify and what not to trust
- Experience setting model governance, monitoring, and responsible AI standards for a team
- Working implementation proficiency across at least two cloud or technical ecosystems, for example AWS and GCP
- A strong quantitative foundation in experimentation, statistical reasoning, and causal thinking
- The ability to align product, engineering, and operations stakeholders on sequencing and trade-offs when the situation is ambiguous
- Experience with retrieval systems, vector search, ranking, recommendation, or production personalization
- Experience with self-hosted or local AI infrastructure, including self-managed agent environments
- Experience in e-commerce, B2B procurement, vendor management, financial products, or heavy integration with external systems
Company Overview
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